Recently,there has been a radial shift from traditional online social networks to content-generated social networks(CGSNs).Contemporary CGSNs,such as Dianping and Trip Advisor,are often the targets of click farming in...Recently,there has been a radial shift from traditional online social networks to content-generated social networks(CGSNs).Contemporary CGSNs,such as Dianping and Trip Advisor,are often the targets of click farming in which fake reviews are posted in order to boost or diminish the ratings of listed products and services simply through clicking.Click farming often emanates from a collection of multiple fake or compromised accounts,which we call click farmers.In this paper,we conduct a three-phase methodology to detect click farming.We begin by clustering communities based on newly-defined collusion networks.We then apply the Louvain community detection method to detecting communities.We finally perform a binary classification on detected-communities.Our results of over a year-long study show that(1)the prevalence of click farming is different across CGSNs;(2)most click farmers are lowly-rated;(3)click-farming communities have relatively tight relations between users;(4)more highly-ranked stores have a greater portion of fake reviews.展开更多
基金supported in part by the National Science Foundation of China,under Grants 71671114,61672350,and U1405251
文摘Recently,there has been a radial shift from traditional online social networks to content-generated social networks(CGSNs).Contemporary CGSNs,such as Dianping and Trip Advisor,are often the targets of click farming in which fake reviews are posted in order to boost or diminish the ratings of listed products and services simply through clicking.Click farming often emanates from a collection of multiple fake or compromised accounts,which we call click farmers.In this paper,we conduct a three-phase methodology to detect click farming.We begin by clustering communities based on newly-defined collusion networks.We then apply the Louvain community detection method to detecting communities.We finally perform a binary classification on detected-communities.Our results of over a year-long study show that(1)the prevalence of click farming is different across CGSNs;(2)most click farmers are lowly-rated;(3)click-farming communities have relatively tight relations between users;(4)more highly-ranked stores have a greater portion of fake reviews.
